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  "##characteristics#run#vqe_input#minimizer_src#all": [
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n"
  ], 
  "##characteristics#run#vqe_input#minimizer_src#all_unique": [
    "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n"
  ], 
  "##characteristics#run#vqe_input#minimizer_src#min": "def my_grid_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples on a grid and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = 3             # Number of points in each dimension\n    num_parameters  = 2 #len(x0)                                # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    xgrid = []\n    fgrid = []\n    for i in range(points):\n        xrow = []\n        frow = []\n        for j in range(points):\n            x = [2*np.pi*i/points, 2*np.pi*j/points]    # Sample a point from a uniform grid\n            fnew = func(x, my_args)\n            flist.append(fnew)\n            xlist.append(x)\n\n            xrow.append(x)\n            frow.append(fnew)\n\n        xgrid.append(xrow)\n        fgrid.append(frow)\n\n\n\n\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n    print(fgrid)\n    FTgrid = np.fft.fft2(fgrid)\n\n    print(FTgrid)\n    def wrp(x):\n        return fn(FTgrid.shape[0], FTgrid, x[0], x[1])\n\n    res = minimize(wrp, best_x, method='nelder-mead', options={'xtol':1e-8, 'disp':False})\n\n    minf = func(res.x, my_args)\n\n    minimizer_output = { 'fun' : minf, 'nfev' : points*points, 'nit' : points*points, 'x' : res.x }\n\n    return minimizer_output\n", 
  "##characteristics#run#vqe_input#minimizer_src#repeats": 10, 
  "##characteristics#run#vqe_input#q_device_name#all": [
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM"
  ], 
  "##characteristics#run#vqe_input#q_device_name#all_unique": [
    "QVM"
  ], 
  "##characteristics#run#vqe_input#q_device_name#min": "QVM", 
  "##characteristics#run#vqe_input#q_device_name#repeats": 10, 
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    50, 
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    50, 
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    50, 
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    50, 
    50, 
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  ], 
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  "##characteristics#run#vqe_input#sample_number#center": 50, 
  "##characteristics#run#vqe_input#sample_number#halfrange": 0, 
  "##characteristics#run#vqe_input#sample_number#max": 50, 
  "##characteristics#run#vqe_input#sample_number#mean": 50.0, 
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  "##characteristics#run#vqe_input#sample_number#repeats": 10, 
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  "##characteristics#run#vqe_output#fun#range_percent": -0.0, 
  "##characteristics#run#vqe_output#fun#repeats": 10, 
  "##characteristics#run#vqe_output#fun_exact#all": [
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    -2.8077777485472413, 
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  "##characteristics#run#vqe_output#fun_exact#halfrange": 2.7814524030667087e-05, 
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  "##characteristics#run#vqe_output#fun_exact#mean": -2.807770029407582, 
  "##characteristics#run#vqe_output#fun_exact#min": -2.8077838766954852, 
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  "##characteristics#run#vqe_output#fun_exact#range_percent": -1.981243945556261e-05, 
  "##characteristics#run#vqe_output#fun_exact#repeats": 10, 
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  "##characteristics#run#vqe_output#fun_validated#repeats": 10, 
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  "##characteristics#run#vqe_output#nfev#halfrange": 0, 
  "##characteristics#run#vqe_output#nfev#max": 9, 
  "##characteristics#run#vqe_output#nfev#mean": 9.0, 
  "##characteristics#run#vqe_output#nfev#min": 9, 
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  ], 
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  "##characteristics#run#vqe_output#nit#center": 9, 
  "##characteristics#run#vqe_output#nit#halfrange": 0, 
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  "##characteristics#run#vqe_output#nit#mean": 9.0, 
  "##characteristics#run#vqe_output#nit#min": 9, 
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    0.16598668365687808, 
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    0.16584703496266184
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  "##characteristics#run#vqe_output#x@0#halfrange": 0.18858011159102542, 
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  "##characteristics#run#vqe_output#x@1#halfrange": 0.18522618447064956, 
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  "##characteristics#run#vqe_output#x@1#min": -0.1681875584159908, 
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  "##characteristics#run#vqe_output#x@1#repeats": 10, 
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    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium", 
    "rigetti-vqe-helium"
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  "##choices#cmd_key#min": "rigetti-vqe-helium", 
  "##choices#cmd_key#repeats": 10, 
  "##choices#compile_type#all": [
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    "dynamic", 
    "dynamic", 
    "dynamic", 
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    "dynamic", 
    "dynamic", 
    "dynamic", 
    "dynamic", 
    "dynamic"
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  "##choices#compile_type#all_unique": [
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  "##choices#console#all": [
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  ], 
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  "##choices#device_id#repeats": 10, 
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    "max", 
    "max", 
    "max", 
    "max", 
    "max", 
    "max", 
    "max", 
    "max", 
    "max"
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  "##choices#env#CK_CPU_FREQUENCY#all_unique": [
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  "##choices#env#CK_CPU_FREQUENCY#min": "max", 
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    "max", 
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  "##choices#env#CK_GPU_FREQUENCY#all_unique": [
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  "##choices#env#CK_GPU_FREQUENCY#min": "max", 
  "##choices#env#CK_GPU_FREQUENCY#repeats": 10, 
  "##choices#env#RIGETTI_QUANTUM_DEVICE#all": [
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    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM", 
    "QVM"
  ], 
  "##choices#env#RIGETTI_QUANTUM_DEVICE#all_unique": [
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  ], 
  "##choices#env#RIGETTI_QUANTUM_DEVICE#min": "QVM", 
  "##choices#env#RIGETTI_QUANTUM_DEVICE#repeats": 10, 
  "##choices#env#VQE_MAX_ITERATIONS#all": [
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    "500", 
    "500", 
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    "500", 
    "500", 
    "500", 
    "500", 
    "500", 
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  "##choices#env#VQE_MAX_ITERATIONS#all_unique": [
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  ], 
  "##choices#env#VQE_MAX_ITERATIONS#min": "500", 
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